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<td valign="baseline" class="function"><b class="function">OAASVM</b>
<td valign="baseline" align="right" class="function"><a href="../svm/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table>
  <p><b>Multi-class SVM using One-Agains-All decomposition.</b></p>
  <hr>
<div class='code'><code>
<span class=help>&nbsp;</span><br>
<span class=help>&nbsp;<span class=help_field>Synopsis:</span></span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;oaasvm(&nbsp;data&nbsp;)</span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;oaasvm(&nbsp;data,&nbsp;options)</span><br>
<span class=help></span><br>
<span class=help>&nbsp;<span class=help_field>Description:</span></span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;oaasvm(&nbsp;data&nbsp;)&nbsp;uses&nbsp;one-agains-all&nbsp;deconposition</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;to&nbsp;train&nbsp;the&nbsp;multi-class&nbsp;Support&nbsp;Vector&nbsp;Machines&nbsp;(SVM)</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;classifier.&nbsp;The&nbsp;classification&nbsp;into&nbsp;nclass&nbsp;classes&nbsp;</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;is&nbsp;decomposed&nbsp;to&nbsp;nclass&nbsp;binary&nbsp;problems.</span><br>
<span class=help></span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;oaasvm(&nbsp;data,&nbsp;options)&nbsp;allows&nbsp;to&nbsp;specify&nbsp;the</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;binary&nbsp;SVM&nbsp;solver&nbsp;and&nbsp;its&nbsp;paramaters.</span><br>
<span class=help></span><br>
<span class=help>&nbsp;<span class=help_field>Input:</span></span><br>
<span class=help>&nbsp;&nbsp;data&nbsp;[struct]&nbsp;Training&nbsp;data:</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.X&nbsp;[dim&nbsp;x&nbsp;num_data]&nbsp;Training&nbsp;vectors.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.y&nbsp;[1&nbsp;x&nbsp;num_data]&nbsp;Labels&nbsp;of&nbsp;training&nbsp;data&nbsp;(1,2,...,nclass).&nbsp;</span><br>
<span class=help></span><br>
<span class=help>&nbsp;&nbsp;options&nbsp;[struct]&nbsp;Control&nbsp;parameters:</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.bin_svm&nbsp;[string]&nbsp;Function&nbsp;which&nbsp;implements&nbsp;the&nbsp;binary&nbsp;SVM&nbsp;</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;solver;&nbsp;(default&nbsp;'smo').</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.verb&nbsp;[1x1]&nbsp;If&nbsp;1&nbsp;then&nbsp;a&nbsp;progress&nbsp;info&nbsp;is&nbsp;displayed&nbsp;(default&nbsp;0).</span><br>
<span class=help>&nbsp;&nbsp;The&nbsp;other&nbsp;fileds&nbsp;of&nbsp;options&nbsp;specifies&nbsp;the&nbsp;options&nbsp;of&nbsp;the&nbsp;binary</span><br>
<span class=help>&nbsp;&nbsp;solver&nbsp;(e.g.,&nbsp;ker,&nbsp;arg,&nbsp;C).&nbsp;See&nbsp;help&nbsp;of&nbsp;the&nbsp;selected&nbsp;solver.</span><br>
<span class=help>&nbsp;&nbsp;</span><br>
<span class=help>&nbsp;<span class=help_field>Output:</span></span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;[struct]&nbsp;Multi-class&nbsp;SVM&nbsp;classifier:</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.Alpha&nbsp;[nsv&nbsp;x&nbsp;nclass]&nbsp;Weights&nbsp;(Lagrangians).</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.b&nbsp;[nclass&nbsp;x&nbsp;1]&nbsp;Biases&nbsp;of&nbsp;discriminant&nbsp;functions.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.sv.X&nbsp;[dim&nbsp;x&nbsp;nsv]&nbsp;Support&nbsp;vectors.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.nsv&nbsp;[1x1]&nbsp;Number&nbsp;of&nbsp;support&nbsp;vectors.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.trnerr&nbsp;[1x1]&nbsp;Training&nbsp;error.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.kercnt&nbsp;[1x1]&nbsp;Number&nbsp;of&nbsp;kernel&nbsp;evaluations.</span><br>
<span class=help>&nbsp;&nbsp;&nbsp;.options&nbsp;[struct[&nbsp;Copy&nbsp;of&nbsp;input&nbsp;argument&nbsp;options.</span><br>
<span class=help></span><br>
<span class=help>&nbsp;<span class=help_field>Example:</span></span><br>
<span class=help>&nbsp;&nbsp;data&nbsp;=&nbsp;load('pentagon');</span><br>
<span class=help>&nbsp;&nbsp;options&nbsp;=&nbsp;struct('ker','rbf','arg',1,'C',10,'verb',1);</span><br>
<span class=help>&nbsp;&nbsp;model&nbsp;=&nbsp;oaasvm(data,options);</span><br>
<span class=help>&nbsp;&nbsp;figure;&nbsp;</span><br>
<span class=help>&nbsp;&nbsp;ppatterns(data);&nbsp;ppatterns(&nbsp;model.sv.X,&nbsp;'ok',13);</span><br>
<span class=help>&nbsp;&nbsp;pboundary(&nbsp;model&nbsp;);</span><br>
<span class=help></span><br>
<span class=help>&nbsp;<span class=also_field>See also </span><span class=also></span><br>
<span class=help><span class=also>&nbsp;&nbsp;<a href = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>,&nbsp;<a href = "../svm/oaosvm.html" target="mdsbody">OAOSVM</a>.</span><br>
<span class=help></span><br>
</code></div>
  <hr>
  <b>Source:</b> <a href= "../svm/list/oaasvm.html">oaasvm.m</a>
  <p><b class="info_field">About: </b>  Statistical Pattern Recognition Toolbox<br>
 (C) 1999-2005, Written by Vojtech Franc and Vaclav Hlavac<br>
 <a href="http://www.cvut.cz">Czech Technical University Prague</a><br>
 <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br>
 <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br>

  <p><b class="info_field">Modifications: </b> <br>
 25-jan-2005, VF, option solver replaced by bin_svm <br>
 27-may-2004, VF, completely re-programed<br>
 18-sep-2001, V. Franc, created<br>

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